Service evaluation suggests variation in clinical care provision in adults with congenital adrenal hyperplasia in the UK and Ireland.

BACKGROUND: Congenital adrenal hyperplasia (CAH) encompasses a rare group of autosomal recessive disorders, characterised by enzymatic defects in steroidogenesis. Heterogeneity in management practices has been observed internationally. The International Congenital Adrenal Hyperplasia registry (I-CAH, https://sdmregistries.org/) was established to enable insights into CAH management and outcomes, yet its global adoption by endocrine centres remains unclear. DESIGN: We sought (1) to assess current practices amongst clinicians managing patients with CAH in the United Kingdom and Ireland, with a focus on choice of glucocorticoid, monitoring practices and screening for associated co-morbidities, and (2) to assess use of the I-CAH registry. MEASUREMENTS: We designed and distributed an anonymised online survey disseminated to members of the Society for Endocrinology and Irish Endocrine Society to capture management practices in the care of patients with CAH. RESULTS: Marked variability was found in CAH management, with differences between general endocrinology and subspecialist settings, particularly in glucocorticoid use, biochemical monitoring and comorbidity screening, with significant disparities in reproductive health monitoring, notably in testicular adrenal rest tumours (TARTs) screening (p = .002), sperm banking (p = .0004) and partner testing for CAH (p < .0001). Adoption of the I-CAH registry was universally low. CONCLUSIONS: Differences in current management of CAH continue to exist. It appears crucial to objectify if different approaches result in different long-term outcomes. New studies such as CaHASE2, incorporating standardised minimum datasets including replacement therapies and monitoring strategies as well as longitudinal data collection, are now needed to define best-practice and standardise care.

Biomarkers in congenital adrenal hyperplasia.

Monitoring of hormone replacement therapy represents a major challenge in the management of congenital adrenal hyperplasia (CAH). In the absence of clear guidance and standardised monitoring strategies, there is no consensus among clinicians regarding the relevance of various biochemical markers used in practice, leading to wide variability in their application and interpretation. In this review, we summarise the published evidence on biochemical monitoring of CAH. We discuss temporal variations of the most commonly measured biomarkers throughout the day, the interrelationship between different biomarkers, as well as their relationship with different glucocorticoid and mineralocorticoid treatment regimens and clinical outcomes. Our review highlights significant heterogeneity across studies in both aims and methodology. However, we identified key messages for the management of patients with CAH. The approach to hormone replacement therapy should be individualised, based on the individual hormonal profile throughout the day in relation to medication. There are limitations to using 17-hydroxyprogesterone, androstenedione and testosterone, and the role of additional biomarkers such 11-oxygenated androgens which are more disease specific should be further established. Noninvasive monitoring via salivary and urinary steroid measurements is becoming increasingly available and should be considered, especially in the management of children with CAH. Additionally, this review indicates the need for large scale longitudinal studies analysing the interrelation between different monitoring strategies used in clinical practice and health outcomes in children and adults with CAH.

Correction: Vargas et al. Solving Schrödinger Bridges via Maximum Likelihood. Entropy 2021, 23, 1134.

In the original publication [...].

Analysis of therapy monitoring in the International Congenital Adrenal Hyperplasia Registry.

OBJECTIVE: Congenital adrenal hyperplasia (CAH) requires exogenous steroid replacement. Treatment is commonly monitored by measuring 17-OH progesterone (17OHP) and androstenedione (D4). DESIGN: Retrospective cohort study using real-world data to evaluate 17OHP and D4 in relation to hydrocortisone (HC) dose in CAH patients treated in 14 countries. PATIENTS: Pseudonymized data from children with 21-hydroxylase deficiency (21OHD) recorded in the International CAH Registry. MEASUREMENTS: Assessments between January 2000 and October 2020 in patients prescribed HC were reviewed to summarise biomarkers 17OHP and D4 and HC dose. Longitudinal assessment of measures was carried out using linear mixed-effects models (LMEM). RESULTS: Cohort of 345 patients, 52.2% female, median age 4.3 years (interquartile range: 3.1-9.2) were taking a median 11.3 mg/m2 /day (8.6-14.4) of HC. Median 17OHP was 35.7 nmol/l (3.0-104.0). Median D4 under 12 years was 0 nmol/L (0-2.0) and above 12 years was 10.5 nmol/L (3.9-21.0). There were significant differences in biomarker values between centres (p < 0.05). Correlation between D4 and 17OHP was good in multiple regression with age (p < 0.001, R2 = 0.29). In longitudinal assessment, 17OHP levels did not change with age, whereas D4 levels increased with age (p < 0.001, R2 = 0.08). Neither biomarker varied directly with dose or weight (p > 0.05). Multivariate LMEM showed HC dose decreasing by 1.0 mg/m2 /day for every 1 point increase in weight standard deviation score. DISCUSSION: Registry data show large variability in 17OHP and D4 between centres. 17OHP correlates with D4 well when accounting for age. Prescribed HC dose per body surface area decreased with weight gain.

Solving Schrödinger Bridges via Maximum Likelihood.

The Schrödinger bridge problem (SBP) finds the most likely stochastic evolution between two probability distributions given a prior stochastic evolution. As well as applications in the natural sciences, problems of this kind have important applications in machine learning such as dataset alignment and hypothesis testing. Whilst the theory behind this problem is relatively mature, scalable numerical recipes to estimate the Schrödinger bridge remain an active area of research. Our main contribution is the proof of equivalence between solving the SBP and an autoregressive maximum likelihood estimation objective. This formulation circumvents many of the challenges of density estimation and enables direct application of successful machine learning techniques. We propose a numerical procedure to estimate SBPs using Gaussian process and demonstrate the practical usage of our approach in numerical simulations and experiments.

How do paediatricians use and monitor antithyroid drugs in the UK? A clinician survey.

OBJECTIVE: We aimed to document current practice in the medical management of paediatric hyperthyroidism in the UK and compare to international recommendations. DESIGN: A 27-question online survey distributed via an electronic newsletter in August 2018. PARTICIPANTS: Responses from 48 members (11%) of the British Society for Paediatric Endocrinology and Diabetes. MEASUREMENTS: Information about antithyroid drug (ATD) preference, treatment duration, monitoring of full blood count (FBC), management of neutropaenia, agranulocytosis screening and patient education. RESULTS: Carbimazole is favoured by 98% of respondents and a "dose titration" regimen preferred over "block and replace" (65% vs 29%). TRAbs (thyroid-stimulating hormone receptor antibodies) are used for diagnostic purposes by 85% and by 33% to look for evidence of disease remission. The majority (81%) treat for a minimum of 2 years before considering a trial off ATD. All respondents reported that they "always/usually" warn their patients about the risk of agranulocytosis before starting ATD, but written information is "rarely/never" provided by 63%. Sore throat (98%) and fever (92%) are the most commonly cited symptoms used to alert a patient to possible agranulocytosis. FBC is measured prior to treatment by 65% and measured periodically during treatment by 70%. CONCLUSIONS: The management of paediatric hyperthyroidism with ATDs in the UK is not consistent with all international recommendations because a block and replace ATD regimen remains widely used. TRAbs are utilized at presentation, but underused for detecting disease remission. National consensus guidelines and written patient information may refine the management of paediatric patients on ATDs.

Efficient inference for sparse latent variable models of transcriptional regulation.

MOTIVATION: Regulation of gene expression in prokaryotes involves complex co-regulatory mechanisms involving large numbers of transcriptional regulatory proteins and their target genes. Uncovering these genome-scale interactions constitutes a major bottleneck in systems biology. Sparse latent factor models, assuming activity of transcription factors (TFs) as unobserved, provide a biologically interpretable modelling framework, integrating gene expression and genome-wide binding data, but at the same time pose a hard computational inference problem. Existing probabilistic inference methods for such models rely on subjective filtering and suffer from scalability issues, thus are not well-suited for realistic genome-scale applications. RESULTS: We present a fast Bayesian sparse factor model, which takes input gene expression and binding sites data, either from ChIP-seq experiments or motif predictions, and outputs active TF-gene links as well as latent TF activities. Our method employs an efficient variational Bayes scheme for model inference enabling its application to large datasets which was not feasible with existing MCMC-based inference methods for such models. We validate our method on synthetic data against a similar model in the literature, employing MCMC for inference, and obtain comparable results with a small fraction of the computational time. We also apply our method to large-scale data from Mycobacterium tuberculosis involving ChIP-seq data on 113 TFs and matched gene expression data for 3863 putative target genes. We evaluate our predictions using an independent transcriptomics experiment involving over-expression of TFs. AVAILABILITY AND IMPLEMENTATION: An easy-to-use Jupyter notebook demo of our method with data is available at https://github.com/zhenwendai/SITAR. CONTACT: mudassar.iqbal@manchester.ac.uk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genome-wide modeling of transcription kinetics reveals patterns of RNA production delays.

Genes with similar transcriptional activation kinetics can display very different temporal mRNA profiles because of differences in transcription time, degradation rate, and RNA-processing kinetics. Recent studies have shown that a splicing-associated RNA production delay can be significant. To investigate this issue more generally, it is useful to develop methods applicable to genome-wide datasets. We introduce a joint model of transcriptional activation and mRNA accumulation that can be used for inference of transcription rate, RNA production delay, and degradation rate given data from high-throughput sequencing time course experiments. We combine a mechanistic differential equation model with a nonparametric statistical modeling approach allowing us to capture a broad range of activation kinetics, and we use Bayesian parameter estimation to quantify the uncertainty in estimates of the kinetic parameters. We apply the model to data from estrogen receptor α activation in the MCF-7 breast cancer cell line. We use RNA polymerase II ChIP-Seq time course data to characterize transcriptional activation and mRNA-Seq time course data to quantify mature transcripts. We find that 11% of genes with a good signal in the data display a delay of more than 20 min between completing transcription and mature mRNA production. The genes displaying these long delays are significantly more likely to be short. We also find a statistical association between high delay and late intron retention in pre-mRNA data, indicating significant splicing-associated production delays in many genes.

A reverse-engineering approach to dissect post-translational modulators of transcription factor's activity from transcriptional data.

BACKGROUND: Transcription factors (TFs) act downstream of the major signalling pathways functioning as master regulators of cell fate. Their activity is tightly regulated at the transcriptional, post-transcriptional and post-translational level. Proteins modifying TF activity are not easily identified by experimental high-throughput methods. RESULTS: We developed a computational strategy, called Differential Multi-Information (DMI), to infer post-translational modulators of a transcription factor from a compendium of gene expression profiles (GEPs). DMI is built on the hypothesis that the modulator of a TF (i.e. kinase/phosphatases), when expressed in the cell, will cause the TF target genes to be co-expressed. On the contrary, when the modulator is not expressed, the TF will be inactive resulting in a loss of co-regulation across its target genes. DMI detects the occurrence of changes in target gene co-regulation for each candidate modulator, using a measure called Multi-Information. We validated the DMI approach on a compendium of 5,372 GEPs showing its predictive ability in correctly identifying kinases regulating the activity of 14 different transcription factors. CONCLUSIONS: DMI can be used in combination with experimental approaches as high-throughput screening to efficiently improve both pathway and target discovery. An on-line web-tool enabling the user to use DMI to identify post-transcriptional modulators of a transcription factor of interest che be found at http://dmi.tigem.it.

Inference of RNA polymerase II transcription dynamics from chromatin immunoprecipitation time course data.

Gene transcription mediated by RNA polymerase II (pol-II) is a key step in gene expression. The dynamics of pol-II moving along the transcribed region influence the rate and timing of gene expression. In this work, we present a probabilistic model of transcription dynamics which is fitted to pol-II occupancy time course data measured using ChIP-Seq. The model can be used to estimate transcription speed and to infer the temporal pol-II activity profile at the gene promoter. Model parameters are estimated using either maximum likelihood estimation or via Bayesian inference using Markov chain Monte Carlo sampling. The Bayesian approach provides confidence intervals for parameter estimates and allows the use of priors that capture domain knowledge, e.g. the expected range of transcription speeds, based on previous experiments. The model describes the movement of pol-II down the gene body and can be used to identify the time of induction for transcriptionally engaged genes. By clustering the inferred promoter activity time profiles, we are able to determine which genes respond quickly to stimuli and group genes that share activity profiles and may therefore be co-regulated. We apply our methodology to biological data obtained using ChIP-seq to measure pol-II occupancy genome-wide when MCF-7 human breast cancer cells are treated with estradiol (E2). The transcription speeds we obtain agree with those obtained previously for smaller numbers of genes with the advantage that our approach can be applied genome-wide. We validate the biological significance of the pol-II promoter activity clusters by investigating cluster-specific transcription factor binding patterns and determining canonical pathway enrichment. We find that rapidly induced genes are enriched for both estrogen receptor alpha (ERα) and FOXA1 binding in their proximal promoter regions.

Detecting regulatory gene-environment interactions with unmeasured environmental factors.

MOTIVATION: Genomic studies have revealed a substantial heritable component of the transcriptional state of the cell. To fully understand the genetic regulation of gene expression variability, it is important to study the effect of genotype in the context of external factors such as alternative environmental conditions. In model systems, explicit environmental perturbations have been considered for this purpose, allowing to directly test for environment-specific genetic effects. However, such experiments are limited to species that can be profiled in controlled environments, hampering their use in important systems such as human. Moreover, even in seemingly tightly regulated experimental conditions, subtle environmental perturbations cannot be ruled out, and hence unknown environmental influences are frequent. Here, we propose a model-based approach to simultaneously infer unmeasured environmental factors from gene expression profiles and use them in genetic analyses, identifying environment-specific associations between polymorphic loci and individual gene expression traits. RESULTS: In extensive simulation studies, we show that our method is able to accurately reconstruct environmental factors and their interactions with genotype in a variety of settings. We further illustrate the use of our model in a real-world dataset in which one environmental factor has been explicitly experimentally controlled. Our method is able to accurately reconstruct the true underlying environmental factor even if it is not given as an input, allowing to detect genuine genotype-environment interactions. In addition to the known environmental factor, we find unmeasured factors involved in novel genotype-environment interactions. Our results suggest that interactions with both known and unknown environmental factors significantly contribute to gene expression variability. AVAILABILITY: and implementation: Software available at http://pmbio.github.io/envGPLVM/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Transcriptomic indices of fast and slow disease progression in two mouse models of amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis is heterogeneous with high variability in the speed of progression even in cases with a defined genetic cause such as superoxide dismutase 1 (SOD1) mutations. We reported that SOD1(G93A) mice on distinct genetic backgrounds (C57 and 129Sv) show consistent phenotypic differences in speed of disease progression and life-span that are not explained by differences in human SOD1 transgene copy number or the burden of mutant SOD1 protein within the nervous system. We aimed to compare the gene expression profiles of motor neurons from these two SOD1(G93A) mouse strains to discover the molecular mechanisms contributing to the distinct phenotypes and to identify factors underlying fast and slow disease progression. Lumbar spinal motor neurons from the two SOD1(G93A) mouse strains were isolated by laser capture microdissection and transcriptome analysis was conducted at four stages of disease. We identified marked differences in the motor neuron transcriptome between the two mice strains at disease onset, with a dramatic reduction of gene expression in the rapidly progressive (129Sv-SOD1(G93A)) compared with the slowly progressing mutant SOD1 mice (C57-SOD1(G93A)) (1276 versus 346; Q-value ≤ 0.01). Gene ontology pathway analysis of the transcriptional profile from 129Sv-SOD1(G93A) mice showed marked downregulation of specific pathways involved in mitochondrial function, as well as predicted deficiencies in protein degradation and axonal transport mechanisms. In contrast, the transcriptional profile from C57-SOD1(G93A) mice with the more benign disease course, revealed strong gene enrichment relating to immune system processes compared with 129Sv-SOD1(G93A) mice. Motor neurons from the more benign mutant strain demonstrated striking complement activation, over-expressing genes normally involved in immune cell function. We validated through immunohistochemistry increased expression of the C3 complement subunit and major histocompatibility complex I within motor neurons. In addition, we demonstrated that motor neurons from the slowly progressing mice activate a series of genes with neuroprotective properties such as angiogenin and the nuclear factor (erythroid-derived 2)-like 2 transcriptional regulator. In contrast, the faster progressing mice show dramatically reduced expression at disease onset of cell pathways involved in neuroprotection. This study highlights a set of key gene and molecular pathway indices of fast or slow disease progression which may prove useful in identifying potential disease modifiers responsible for the heterogeneity of human amyotrophic lateral sclerosis and which may represent valid therapeutic targets for ameliorating the disease course in humans.

Genome-wide occupancy links Hoxa2 to Wnt-ß-catenin signaling in mouse embryonic development.

The regulation of gene expression is central to developmental programs and largely depends on the binding of sequence-specific transcription factors with cis-regulatory elements in the genome. Hox transcription factors specify the spatial coordinates of the body axis in all animals with bilateral symmetry, but a detailed knowledge of their molecular function in instructing cell fates is lacking. Here, we used chromatin immunoprecipitation with massively parallel sequencing (ChIP-seq) to identify Hoxa2 genomic locations in a time and space when it is actively instructing embryonic development in mouse. Our data reveals that Hoxa2 has large genome coverage and potentially regulates thousands of genes. Sequence analysis of Hoxa2-bound regions identifies high occurrence of two main classes of motifs, corresponding to Hox and Pbx-Hox recognition sequences. Examination of the binding targets of Hoxa2 faithfully captures the processes regulated by Hoxa2 during embryonic development; in addition, it uncovers a large cluster of potential targets involved in the Wnt-signaling pathway. In vivo examination of canonical Wnt-ß-catenin signaling reveals activity specifically in Hoxa2 domain of expression, and this is undetectable in Hoxa2 mutant embryos. The comprehensive mapping of Hoxa2-binding sites provides a framework to study Hox regulatory networks in vertebrate developmental processes.

National service evaluation of the quality of care for children and young people with congenital adrenal hyperplasia in the United Kingdom: survey responses from patients and clinicians.

INTRODUCTION: Quantifying differences in service provision for children and young people (CYP) living with Congenital Adrenal Hyperplasia (CAH) across the United Kingdom. METHODS: A national service evaluation using online questionnaires circulated to patients and clinicians from secondary and tertiary UK centres managing CYP with CAH, and via the "Living with CAH" support group mailing list. RESULTS: Total of 195 responses relating to patients aged 0-20 years attending 33 clinics (43 patients, 152 carers), as well as 34 clinicians from 18 trusts working across the 33 clinics. Only 12% of clinicians were 'completely satisfied' with the service provided, compared to 68% of carers and 76% of patients. Whilst 94% of clinicians reported providing formal training to families with CAH, over 80% of both patients and carers reported not attending what they considered formal training. Appetite for further training was higher in carers (86%) than patients (55%), although further 'unsure' responses suggested formal training sessions would likely be well attended. Access to psychological services was difficult for 44% of clinicians. Biochemical monitoring of treatment was broadly in keeping with international guidelines, with 67% of clinicians reporting regular use of dried blood spots, and 12% regular urinary steroid metabolites. CONCLUSION: While there is overall good satisfaction with care provision among patients and carers with CAH in the UK, extra resources addressing the psychological and educational needs about the disease and its management would benefit patients and carers. Improved access to allied health professionals and psychologists will help support families and improve patient outcomes.

Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters.

BACKGROUND: Time course data from microarrays and high-throughput sequencing experiments require simple, computationally efficient and powerful statistical models to extract meaningful biological signal, and for tasks such as data fusion and clustering. Existing methodologies fail to capture either the temporal or replicated nature of the experiments, and often impose constraints on the data collection process, such as regularly spaced samples, or similar sampling schema across replications. RESULTS: We propose hierarchical Gaussian processes as a general model of gene expression time-series, with application to a variety of problems. In particular, we illustrate the method's capacity for missing data imputation, data fusion and clustering.The method can impute data which is missing both systematically and at random: in a hold-out test on real data, performance is significantly better than commonly used imputation methods. The method's ability to model inter- and intra-cluster variance leads to more biologically meaningful clusters. The approach removes the necessity for evenly spaced samples, an advantage illustrated on a developmental Drosophila dataset with irregular replications. CONCLUSION: The hierarchical Gaussian process model provides an excellent statistical basis for several gene-expression time-series tasks. It has only a few additional parameters over a regular GP, has negligible additional complexity, is easily implemented and can be integrated into several existing algorithms. Our experiments were implemented in python, and are available from the authors' website: http://staffwww.dcs.shef.ac.uk/people/J.Hensman/.

Unravelling the enigma of selective vulnerability in neurodegeneration: motor neurons resistant to degeneration in ALS show distinct gene expression characteristics and decreased susceptibility to excitotoxicity.

A consistent clinical feature of amyotrophic lateral sclerosis (ALS) is the sparing of eye movements and the function of external sphincters, with corresponding preservation of motor neurons in the brainstem oculomotor nuclei, and of Onuf's nucleus in the sacral spinal cord. Studying the differences in properties of neurons that are vulnerable and resistant to the disease process in ALS may provide insights into the mechanisms of neuronal degeneration, and identify targets for therapeutic manipulation. We used microarray analysis to determine the differences in gene expression between oculomotor and spinal motor neurons, isolated by laser capture microdissection from the midbrain and spinal cord of neurologically normal human controls. We compared these to transcriptional profiles of oculomotor nuclei and spinal cord from rat and mouse, obtained from the GEO omnibus database. We show that oculomotor neurons have a distinct transcriptional profile, with significant differential expression of 1,757 named genes (q < 0.001). Differentially expressed genes are enriched for the functional categories of synaptic transmission, ubiquitin-dependent proteolysis, mitochondrial function, transcriptional regulation, immune system functions, and the extracellular matrix. Marked differences are seen, across the three species, in genes with a function in synaptic transmission, including several glutamate and GABA receptor subunits. Using patch clamp recording in acute spinal and brainstem slices, we show that resistant oculomotor neurons show a reduced AMPA-mediated inward calcium current, and a higher GABA-mediated chloride current, than vulnerable spinal motor neurons. The findings suggest that reduced susceptibility to excitotoxicity, mediated in part through enhanced GABAergic transmission, is an important determinant of the relative resistance of oculomotor neurons to degeneration in ALS.

Joint modelling of confounding factors and prominent genetic regulators provides increased accuracy in genetical genomics studies.

Expression quantitative trait loci (eQTL) studies are an integral tool to investigate the genetic component of gene expression variation. A major challenge in the analysis of such studies are hidden confounding factors, such as unobserved covariates or unknown subtle environmental perturbations. These factors can induce a pronounced artifactual correlation structure in the expression profiles, which may create spurious false associations or mask real genetic association signals. Here, we report PANAMA (Probabilistic ANAlysis of genoMic dAta), a novel probabilistic model to account for confounding factors within an eQTL analysis. In contrast to previous methods, PANAMA learns hidden factors jointly with the effect of prominent genetic regulators. As a result, this new model can more accurately distinguish true genetic association signals from confounding variation. We applied our model and compared it to existing methods on different datasets and biological systems. PANAMA consistently performs better than alternative methods, and finds in particular substantially more trans regulators. Importantly, our approach not only identifies a greater number of associations, but also yields hits that are biologically more plausible and can be better reproduced between independent studies. A software implementation of PANAMA is freely available online at http://ml.sheffield.ac.uk/qtl/.

Modeling meiotic chromosomes indicates a size dependent contribution of telomere clustering and chromosome rigidity to homologue juxtaposition.

Meiosis is the cell division that halves the genetic component of diploid cells to form gametes or spores. To achieve this, meiotic cells undergo a radical spatial reorganisation of chromosomes. This reorganisation is a prerequisite for the pairing of parental homologous chromosomes and the reductional division, which halves the number of chromosomes in daughter cells. Of particular note is the change from a centromere clustered layout (Rabl configuration) to a telomere clustered conformation (bouquet stage). The contribution of the bouquet structure to homologous chromosome pairing is uncertain. We have developed a new in silico model to represent the chromosomes of Saccharomyces cerevisiae in space, based on a worm-like chain model constrained by attachment to the nuclear envelope and clustering forces. We have asked how these constraints could influence chromosome layout, with particular regard to the juxtaposition of homologous chromosomes and potential nonallelic, ectopic, interactions. The data support the view that the bouquet may be sufficient to bring short chromosomes together, but the contribution to long chromosomes is less. We also find that persistence length is critical to how much influence the bouquet structure could have, both on pairing of homologues and avoiding contacts with heterologues. This work represents an important development in computer modeling of chromosomes, and suggests new explanations for why elucidating the functional significance of the bouquet by genetics has been so difficult.

Model-based method for transcription factor target identification with limited data.

We present a computational method for identifying potential targets of a transcription factor (TF) using wild-type gene expression time series data. For each putative target gene we fit a simple differential equation model of transcriptional regulation, and the model likelihood serves as a score to rank targets. The expression profile of the TF is modeled as a sample from a Gaussian process prior distribution that is integrated out using a nonparametric Bayesian procedure. This results in a parsimonious model with relatively few parameters that can be applied to short time series datasets without noticeable overfitting. We assess our method using genome-wide chromatin immunoprecipitation (ChIP-chip) and loss-of-function mutant expression data for two TFs, Twist, and Mef2, controlling mesoderm development in Drosophila. Lists of top-ranked genes identified by our method are significantly enriched for genes close to bound regions identified in the ChIP-chip data and for genes that are differentially expressed in loss-of-function mutants. Targets of Twist display diverse expression profiles, and in this case a model-based approach performs significantly better than scoring based on correlation with TF expression. Our approach is found to be comparable or superior to ranking based on mutant differential expression scores. Also, we show how integrating complementary wild-type spatial expression data can further improve target ranking performance.

Blood Pressure in Children with Congenital Adrenal Hyperplasia Due To 21-Hydroxylase Deficiency.

Congenital adrenal hyperplasia (CAH) due to 21-hydroxylase deficiency (21OHD) leads to impaired glucocorticoid and mineralocorticoid synthesis with excess production of androgens. Replication of the normal circadian cortisol secretion is challenging and supraphysiological doses of glucocorticoids are often required. Most patients experience transient episodes of hyper- and hypocortisolaemia during the day leading to adverse metabolic outcomes such as insulin resistance, visceral adiposity and cardiovascular morbidity, including hypertension. These health problems are commonly diagnosed in adolescence and adulthood. Herein, we review the published data on the variation in blood pressure in CAH due to 21OHD and the interrelation with disease and treatment factors.